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Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease detection. Incorrect and delayed diagnosis can result in either excessive or inadequate use of chemicals, with increased production costs, environmental, and health impacts. We have manually captured 3,651 high-quality, real-life symptom images of multiple apple foliar diseases, with variable illumination, angles, surfaces, and noise. A subset, expert-annotated to create a pilot dataset for apple scab, cedar apple rust, and healthy leaves, was made available to the Kaggle community for 'Plant Pathology Challenge'; part of the Fine-Grained Visual Categorization (FGVC) workshop at CVPR 2020 (Computer Vision and Pattern Recognition). We also trained an off-the-shelf convolutional neural network (CNN) on this data for disease classification and achieved 97% accuracy on a held-out test set. This dataset will contribute towards development and deployment of machine learning-based automated plant disease classification algorithms to ultimately realize fast and accurate disease detection. We will continue to add images to the pilot dataset for a larger, more comprehensive expert-annotated dataset for future Kaggle competitions and to explore more advanced methods for disease classification and quantification.